POS1597-HPR ASSESSING THE USE OF VOICE COMMANDS IN THE CLINICAL SETTING
Authors: J. Martinho, R. Andev, R. Luqmani
Journal: Annals of the Rheumatic Diseases (2023)

<h3>Background</h3> Artificial intelligence (AI) could reduce the administrative workload of clinicians, thus allowing them to focus more on clinical work. AI-powered voice-recognition programs can record appointments and request additional diagnostic tests through automatic flows generated by specific voice commands, reducing the administrative burden. Although some clinicians consider these programs helpful in the clinical setting, others consider them challenging to learn and not to save time. <h3>Objectives</h3> To investigate the impact of voice commands in clinical practice: i) on time spent on different tasks (clinical and administrative) during the appointment, ii) on the accuracy of records, iii) on patients' and clinicians' satisfaction. <h3>Methods</h3> We undertook a single-centre prospective quality improvement project between 26/10/22 and 20/12/22 in the rheumatology department of Nuffield Orthopaedic Centre - Oxford University Hospitals. We included patients attending general rheumatology (GR), complex musculoskeletal (CMSK) and vasculitis clinics, performed by clinicians using voice commands (group 1, N=1) and clinicians not using voice commands (group 2, N=4). The voice command program used was Dragon Medical One. We defined "using voice commands" when this tool was used to insert a pre-prepared text template or order diagnostic exams. One investigator attended the clinics and timed the tasks performed during the appointment. Clinicians and patients answered questionnaires attributing satisfaction scores from 0 (very unsatisfied) to 10 (very satisfied). Univariate analysis was performed, as appropriate, using SPSSV25. <h3>Results</h3> Data regarding 80 appointments were collected: 40 from group 1 and 40 from group 2. The proportion of the clinic types in groups 1 and 2 was: GR 52.5% vs. 0%; CMSK 0% vs. 40%; and vasculitis 47.5% vs. 60%, respectively. There were no differences between the groups regarding new and follow-up patients or face-to-face and phone clinics. Group 1 finished recording the consult in a shorter time than group 2 (mean±SD: 5.6±1.8min vs. 8.9±3.7min, p&lt;0.001). The time spent with the patient (mean±SD: 16.9±7.0 vs. 17.7±7.6, p=0.637) and ordering diagnostic tests (mean±SD: 2.9±1.7 vs. 2.7±1.2, p=0.803), did not differ between the groups. To compare the groups performing the same type of clinic, we conducted a subanalysis including only the vasculitis clinics (group 1 N=19 and group 2 N=24). In this setting, group 1 also finished recording the vasculitis consultations in a shorter time than group 2 (mean±SD: 5.2±1.6min vs. 8.9±4.5min, p&lt;0.001). Moreover, group 1 had more final records with spelling mistakes than group 2 (26 vs. 13, p=0.004) but no more clinically significant errors (8 vs. 3, p=0.105). The latter consisted of one drug dose error, seven nonsense sentences from group 1, and 3 nonsense sentences from group 2. Regarding the clinicians' satisfaction levels, 5 out of 7 items evaluated showed significant differences between both groups. Group 1 had higher median scores of satisfaction regarding how the consultation went (9 [1] vs. 8 [1], p&lt;0.001); the time they had to address the patient's concerns (9 [1] vs. 8 [2], p&lt;0.001); and the way the dictation worked (8 [1] vs. 6 [1.75], p&lt;0.001), compared to group 2. Moreover, group 1 felt that they spent more time listening and talking to the patient as opposed to doing administrative work (8 [1] vs. 7 [3], p&lt;0.001), and also considered they had to do less writing (2 [1] vs. 5 [2], p&lt;0.001), than group 2. There was no difference between the groups concerning patient satisfaction levels. <h3>Conclusion</h3> We report that voice commands: (i) were useful in reducing the time spent recording the consultation but did not influence the time spent with the patient or the time spent ordering diagnostic tests; (ii) resulted in a higher number of spelling mistakes but not clinically significant errors; and (iii) improved the clinicians' satisfaction at most levels and did not influence patients' satisfaction. Medical staff should be trained to adopt this technology, and developers should focus on improving accuracy and misspelling minimization. <h3> Acknowledgements</h3> Dr. Shirish Dubey, Dr. Ayna Verdiyeva, Dr. Lija James. <h3>Disclosure of Interests</h3> None Declared.